6 repository-uri
Standards for structuring data for machine learning.
Distinguishing note: Focuses on array and matrix formats.
Explore 6 awesome GitHub repositories matching artificial intelligence & ml · Data Representation. Refine with filters or upvote what's useful.
This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
Formats data into structured arrays for model compatibility.
This project is a comprehensive Chinese translation of a technical deep learning textbook, providing an educational resource on the theory and implementation of neural networks. It functions as a collaborative technical translation project designed to make complex academic AI literature accessible to non-English speakers. The project utilizes a community-driven translation model that integrates external suggestions and pull requests to refine linguistic accuracy and reduce bias. It employs standardized terminology mapping to ensure a uniform vocabulary throughout the translated content. To i
Discusses encoding concepts using combinations of multiple features for efficient representation.
Paddle is a deep learning framework designed for building, training, and deploying neural networks. It provides a platform for constructing models using tensor-based computations and supports both dynamic and static execution graphs to facilitate research and production workflows. The platform functions as a distributed machine learning system, enabling the scaling of training workloads across multiple nodes and hardware clusters. It includes a comprehensive toolkit for model deployment and optimization, allowing users to convert external model formats, compress trained models for resource-co
Organizes multidimensional numerical data into structured arrays as the fundamental building block for all computations.
This project is a curated library of hand-drawn technical documentation and visual knowledge bases designed to simplify complex software engineering concepts. It replaces traditional code-centric diagrams with annotated illustrations and sketchnotes to translate abstract logic into intuitive mental models. The resource utilizes an analogy-based learning approach, mapping software operations and algorithms to concrete physical metaphors. It employs a visual-first documentation model that breaks down intricate technical workflows into sequential sketches for step-by-step comprehension. The kno
Represents data structures and version control operations through physical analogies rather than code-centric diagrams.
GoLearn is a machine learning library for the Go programming language. It provides a supervised learning framework and a toolkit for building, training, and evaluating predictive models through a standardized interface. The project implements a data frame system that loads CSV files into structured grids for matrix operations. It includes a preprocessing library for discretizing continuous variables and a model evaluation toolkit that utilizes confusion matrices and cross-validation to measure precision and recall. The library covers data engineering and management, including the ability to
Implements a matrix-based grid structure to store dataset attributes and instances for efficient numerical operations.
FriendsDontLetFriends is a scientific data visualization guide and framework designed to help users create accurate plots while avoiding common data representation mistakes. It provides a collection of scripts and guidelines for selecting distribution plots, color scales, and layouts that accurately represent complex experimental data. The project distinguishes itself through specialized toolkits for revealing hidden patterns in large datasets. It includes systems for heatmap optimization via dimension reordering and outlier management, as well as spatial layout algorithms to improve the inte
Provides best practices for representing fractional population data using stacked bars for higher accuracy.